Overview

Dataset statistics

Number of variables29
Number of observations37813
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 MiB
Average record size in memory232.0 B

Variable types

NUM15
CAT9
BOOL5

Warnings

city has a high cardinality: 902 distinct values High cardinality
total_land_area is highly correlated with garden_areaHigh correlation
garden_area is highly correlated with total_land_areaHigh correlation
subtype_of_property is highly correlated with type_of_property and 1 other fieldsHigh correlation
type_of_property is highly correlated with subtype_of_property and 1 other fieldsHigh correlation
province is highly correlated with region and 1 other fieldsHigh correlation
region is highly correlated with province and 1 other fieldsHigh correlation
type_of_property_num is highly correlated with type_of_property and 1 other fieldsHigh correlation
region_num is highly correlated with region and 1 other fieldsHigh correlation
area is highly skewed (γ1 = 21.46038373) Skewed
terrace_area is highly skewed (γ1 = 113.1304659) Skewed
garden_area is highly skewed (γ1 = 88.06374136) Skewed
total_land_area is highly skewed (γ1 = 84.15953068) Skewed
id has unique values Unique
nr_of_rooms has 730 (1.9%) zeros Zeros
garden_area has 20370 (53.9%) zeros Zeros

Reproduction

Analysis started2020-12-07 08:38:17.343852
Analysis finished2020-12-07 08:39:41.799130
Duration1 minute and 24.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct37813
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8852120.03
Minimum1882546
Maximum9066628
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:42.223054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1882546
5-th percentile8271417.6
Q18825271
median8954685
Q39020304
95-th percentile9057871.4
Maximum9066628
Range7184082
Interquartile range (IQR)195033

Descriptive statistics

Standard deviation327088.2773
Coefficient of variation (CV)0.03695027588
Kurtosis50.31477971
Mean8852120.03
Median Absolute Deviation (MAD)79521
Skewness-5.19368627
Sum3.347252147e+11
Variance1.069867411e+11
MonotocityNot monotonic
2020-12-07T09:39:42.507712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89390451< 0.1%
 
89577541< 0.1%
 
89245591< 0.1%
 
89880461< 0.1%
 
90576761< 0.1%
 
90231841< 0.1%
 
90105651< 0.1%
 
82665351< 0.1%
 
90638111< 0.1%
 
89306901< 0.1%
 
Other values (37803)37803> 99.9%
 
ValueCountFrequency (%) 
18825461< 0.1%
 
23357391< 0.1%
 
27849381< 0.1%
 
30011351< 0.1%
 
37945241< 0.1%
 
ValueCountFrequency (%) 
90666281< 0.1%
 
90665561< 0.1%
 
90665271< 0.1%
 
90664171< 0.1%
 
90663991< 0.1%
 

locality
Real number (ℝ≥0)

Distinct1014
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5138.92127
Minimum1000
Maximum9992
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:42.909026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1070
Q12100
median4671
Q38400
95-th percentile9500
Maximum9992
Range8992
Interquartile range (IQR)6300

Descriptive statistics

Standard deviation3118.850063
Coefficient of variation (CV)0.6069075393
Kurtosis-1.618424221
Mean5138.92127
Median Absolute Deviation (MAD)3112
Skewness0.08205409166
Sum194318030
Variance9727225.715
MonotocityNot monotonic
2020-12-07T09:39:43.340528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
830010512.8%
 
11807281.9%
 
10006431.7%
 
84005811.5%
 
90005381.4%
 
20004741.3%
 
10504431.2%
 
83704151.1%
 
10704061.1%
 
86703430.9%
 
Other values (1004)3219185.1%
 
ValueCountFrequency (%) 
10006431.7%
 
10201250.3%
 
10303240.9%
 
10401520.4%
 
10504431.2%
 
ValueCountFrequency (%) 
99923< 0.1%
 
9991690.2%
 
9990620.2%
 
99889< 0.1%
 
99824< 0.1%
 

type_of_property
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
APARTMENT
20229 
HOUSE
17584 
ValueCountFrequency (%) 
APARTMENT2022953.5%
 
HOUSE1758446.5%
 
2020-12-07T09:39:43.642625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:43.782997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:44.101868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length7.139898977
Min length5

subtype_of_property
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
APARTMENT
15399 
HOUSE
12906 
VILLA
2594 
DUPLEX
 
1306
GROUND_FLOOR
 
1109
Other values (17)
4499 
ValueCountFrequency (%) 
APARTMENT1539940.7%
 
HOUSE1290634.1%
 
VILLA25946.9%
 
DUPLEX13063.5%
 
GROUND_FLOOR11092.9%
 
PENTHOUSE9702.6%
 
FLAT_STUDIO7201.9%
 
MIXED_USE_BUILDING4461.2%
 
EXCEPTIONAL_PROPERTY4281.1%
 
SERVICE_FLAT3200.8%
 
Other values (12)16154.3%
 
2020-12-07T09:39:44.598404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:44.897122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length9
Mean length7.617671171
Min length3

price
Real number (ℝ≥0)

Distinct3591
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393353.9229
Minimum2500
Maximum9500000
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:45.288168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile125000
Q1209500
median285000
Q3409825
95-th percentile995000
Maximum9500000
Range9497500
Interquartile range (IQR)200325

Descriptive statistics

Standard deviation419522.594
Coefficient of variation (CV)1.066527037
Kurtosis53.08026823
Mean393353.9229
Median Absolute Deviation (MAD)90280
Skewness5.704647086
Sum1.487389189e+10
Variance1.759992069e+11
MonotocityNot monotonic
2020-12-07T09:39:45.711122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2490005141.4%
 
2990004811.3%
 
1990004761.3%
 
2750004611.2%
 
2950004411.2%
 
2250004081.1%
 
3950003871.0%
 
2350003811.0%
 
1950003811.0%
 
1750003801.0%
 
Other values (3581)3350388.6%
 
ValueCountFrequency (%) 
25003< 0.1%
 
40001< 0.1%
 
99991< 0.1%
 
100003< 0.1%
 
118251< 0.1%
 
ValueCountFrequency (%) 
95000001< 0.1%
 
87500001< 0.1%
 
85000001< 0.1%
 
77500001< 0.1%
 
65000004< 0.1%
 

nr_of_rooms
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.740221617
Minimum0
Maximum18
Zeros730
Zeros (%)1.9%
Memory size295.4 KiB
2020-12-07T09:39:45.964881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.350243159
Coefficient of variation (CV)0.4927496195
Kurtosis7.135375623
Mean2.740221617
Median Absolute Deviation (MAD)1
Skewness1.497759931
Sum103616
Variance1.823156589
MonotocityNot monotonic
2020-12-07T09:39:46.207610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
21272233.6%
 
31210832.0%
 
4484012.8%
 
1424811.2%
 
519315.1%
 
07301.9%
 
67141.9%
 
72410.6%
 
81290.3%
 
9540.1%
 
Other values (5)960.3%
 
ValueCountFrequency (%) 
07301.9%
 
1424811.2%
 
21272233.6%
 
31210832.0%
 
4484012.8%
 
ValueCountFrequency (%) 
185< 0.1%
 
156< 0.1%
 
12230.1%
 
11200.1%
 
10420.1%
 

area
Real number (ℝ≥0)

SKEWED

Distinct737
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.1854918
Minimum5
Maximum11366
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:46.522977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile55
Q190
median125
Q3185
95-th percentile375
Maximum11366
Range11361
Interquartile range (IQR)95

Descriptive statistics

Standard deviation146.335724
Coefficient of variation (CV)0.9192780218
Kurtosis1290.644734
Mean159.1854918
Median Absolute Deviation (MAD)42
Skewness21.46038373
Sum6019281
Variance21414.14411
MonotocityNot monotonic
2020-12-07T09:39:46.801242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
908122.1%
 
1008102.1%
 
1207151.9%
 
806721.8%
 
1106491.7%
 
1506311.7%
 
856131.6%
 
1405921.6%
 
705461.4%
 
2005411.4%
 
Other values (727)3123282.6%
 
ValueCountFrequency (%) 
51< 0.1%
 
131< 0.1%
 
141< 0.1%
 
152< 0.1%
 
167< 0.1%
 
ValueCountFrequency (%) 
113661< 0.1%
 
87501< 0.1%
 
43801< 0.1%
 
40001< 0.1%
 
35003< 0.1%
 

equiped_kitchen
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
INSTALLED
13832 
UNK
11324 
HYPER_EQUIPPED
5778 
SEMI_EQUIPPED
2617 
USA_HYPER_EQUIPPED
1921 
Other values (4)
2341 
ValueCountFrequency (%) 
INSTALLED1383236.6%
 
UNK1132429.9%
 
HYPER_EQUIPPED577815.3%
 
SEMI_EQUIPPED26176.9%
 
USA_HYPER_EQUIPPED19215.1%
 
NOT_INSTALLED14363.8%
 
USA_INSTALLED7261.9%
 
USA_SEMI_EQUIPPED1590.4%
 
USA_UNINSTALLED200.1%
 
2020-12-07T09:39:47.065114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:47.228373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:47.471132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length8.966757464
Min length3

open_fire
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
0
35747 
1
 
2066
ValueCountFrequency (%) 
03574794.5%
 
120665.5%
 
2020-12-07T09:39:47.615117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

terrace
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
1
24075 
0
13738 
ValueCountFrequency (%) 
12407563.7%
 
01373836.3%
 
2020-12-07T09:39:47.692046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

terrace_area
Real number (ℝ)

SKEWED

Distinct208
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4556105
Minimum-1
Maximum20000
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:47.874455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q312
95-th percentile50
Maximum20000
Range20001
Interquartile range (IQR)13

Descriptive statistics

Standard deviation129.897641
Coefficient of variation (CV)11.33921592
Kurtosis15747.18542
Mean11.4556105
Median Absolute Deviation (MAD)0
Skewness113.1304659
Sum433171
Variance16873.39714
MonotocityNot monotonic
2020-12-07T09:39:48.208779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-12234159.1%
 
108772.3%
 
208382.2%
 
67241.9%
 
156781.8%
 
126621.8%
 
86601.7%
 
305791.5%
 
95401.4%
 
55201.4%
 
Other values (198)939424.8%
 
ValueCountFrequency (%) 
-12234159.1%
 
1670.2%
 
23000.8%
 
33560.9%
 
45101.3%
 
ValueCountFrequency (%) 
200001< 0.1%
 
80002< 0.1%
 
60001< 0.1%
 
35001< 0.1%
 
34001< 0.1%
 

garden
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
0
19037 
1
18776 
ValueCountFrequency (%) 
01903750.3%
 
11877649.7%
 
2020-12-07T09:39:48.425475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

garden_area
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2943
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683.1153043
Minimum-1
Maximum1134500
Zeros20370
Zeros (%)53.9%
Memory size295.4 KiB
2020-12-07T09:39:48.733740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q3280
95-th percentile2000
Maximum1134500
Range1134501
Interquartile range (IQR)280

Descriptive statistics

Standard deviation8745.473068
Coefficient of variation (CV)12.80233807
Kurtosis10017.43051
Mean683.1153043
Median Absolute Deviation (MAD)0
Skewness88.06374136
Sum25830639
Variance76483299.18
MonotocityNot monotonic
2020-12-07T09:39:49.090952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02037053.9%
 
1002420.6%
 
2001920.5%
 
501670.4%
 
3001530.4%
 
-11360.4%
 
4001330.4%
 
1501330.4%
 
601300.3%
 
801280.3%
 
Other values (2933)1602942.4%
 
ValueCountFrequency (%) 
-11360.4%
 
02037053.9%
 
1750.2%
 
2240.1%
 
3260.1%
 
ValueCountFrequency (%) 
11345001< 0.1%
 
8494501< 0.1%
 
3958501< 0.1%
 
3126001< 0.1%
 
2487001< 0.1%
 

total_land_area
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3201
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean883.6929892
Minimum5
Maximum1135150
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:49.474265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile60
Q1100
median170
Q3482
95-th percentile2401.2
Maximum1135150
Range1135145
Interquartile range (IQR)382

Descriptive statistics

Standard deviation8900.922236
Coefficient of variation (CV)10.07241468
Kurtosis9361.911167
Mean883.6929892
Median Absolute Deviation (MAD)92
Skewness84.15953068
Sum33415083
Variance79226416.65
MonotocityNot monotonic
2020-12-07T09:39:49.788183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
904551.2%
 
1004521.2%
 
804131.1%
 
1103791.0%
 
953681.0%
 
1203661.0%
 
853410.9%
 
703230.9%
 
753140.8%
 
1053020.8%
 
Other values (3191)3410090.2%
 
ValueCountFrequency (%) 
51< 0.1%
 
131< 0.1%
 
141< 0.1%
 
152< 0.1%
 
167< 0.1%
 
ValueCountFrequency (%) 
11351501< 0.1%
 
8500001< 0.1%
 
3963001< 0.1%
 
3128411< 0.1%
 
2500001< 0.1%
 

nr_of_facades
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.729140772
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:50.058527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q33
95-th percentile4
Maximum10
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.875176636
Coefficient of variation (CV)1.084455741
Kurtosis-1.220394999
Mean1.729140772
Median Absolute Deviation (MAD)1
Skewness-0.4486850723
Sum65384
Variance3.516287416
MonotocityNot monotonic
2020-12-07T09:39:50.252729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
21231332.6%
 
-11078228.5%
 
4796621.1%
 
3645517.1%
 
12950.8%
 
101< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
-11078228.5%
 
12950.8%
 
21231332.6%
 
3645517.1%
 
4796621.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
61< 0.1%
 
4796621.1%
 
3645517.1%
 
21231332.6%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
0
36780 
1
 
1033
ValueCountFrequency (%) 
03678097.3%
 
110332.7%
 
2020-12-07T09:39:51.408557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
AS_NEW
11598 
UKN
10120 
GOOD
9806 
TO_BE_DONE_UP
2238 
TO_RENOVATE
2029 
Other values (2)
2022 
ValueCountFrequency (%) 
AS_NEW1159830.7%
 
UKN1012026.8%
 
GOOD980625.9%
 
TO_BE_DONE_UP22385.9%
 
TO_RENOVATE20295.4%
 
JUST_RENOVATED19055.0%
 
TO_RESTORE1170.3%
 
2020-12-07T09:39:51.564754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:51.718940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:51.923663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length4
Mean length5.776452543
Min length3

kitchen
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
0
25033 
1
12780 
ValueCountFrequency (%) 
02503366.2%
 
11278033.8%
 
2020-12-07T09:39:52.142534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

region
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
VLA
23667 
WAL
9538 
BXL
4608 
ValueCountFrequency (%) 
VLA2366762.6%
 
WAL953825.2%
 
BXL460812.2%
 
2020-12-07T09:39:52.297001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:52.481265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:52.628500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

province
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
VWV
7024 
VAN
5737 
VOV
5043 
BXL
4608 
WHT
4070 
Other values (6)
11331 
ValueCountFrequency (%) 
VWV702418.6%
 
VAN573715.2%
 
VOV504313.3%
 
BXL460812.2%
 
WHT407010.8%
 
VBR397410.5%
 
VLI18895.0%
 
WBR16004.2%
 
WNA14933.9%
 
WLG12723.4%
 
2020-12-07T09:39:52.829938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:53.065710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

sq_m_price
Real number (ℝ≥0)

Distinct16834
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2666.741691
Minimum4.17
Maximum33000
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:53.319588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.17
5-th percentile977.78
Q11753.24
median2372.55
Q33105.26
95-th percentile5186.942
Maximum33000
Range32995.83
Interquartile range (IQR)1352.02

Descriptive statistics

Standard deviation1661.788105
Coefficient of variation (CV)0.6231530073
Kurtosis26.85575002
Mean2666.741691
Median Absolute Deviation (MAD)669.41
Skewness3.7751219
Sum100837503.6
Variance2761539.705
MonotocityNot monotonic
2020-12-07T09:39:53.561170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25002450.6%
 
30001680.4%
 
20001400.4%
 
1666.671000.3%
 
1500910.2%
 
3500850.2%
 
1000830.2%
 
2250740.2%
 
2333.33720.2%
 
2750670.2%
 
Other values (16824)3668897.0%
 
ValueCountFrequency (%) 
4.171< 0.1%
 
10.921< 0.1%
 
15.431< 0.1%
 
30.331< 0.1%
 
42.861< 0.1%
 
ValueCountFrequency (%) 
330001< 0.1%
 
31976.741< 0.1%
 
270001< 0.1%
 
23629.231< 0.1%
 
21739.111< 0.1%
 

sq_m_land_price
Real number (ℝ≥0)

Distinct22878
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1947.327265
Minimum0.75
Maximum33000
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:53.817980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile182.35
Q1655.74
median1766.13
Q32663.04
95-th percentile4561.25
Maximum33000
Range32999.25
Interquartile range (IQR)2007.3

Descriptive statistics

Standard deviation1738.105625
Coefficient of variation (CV)0.8925595896
Kurtosis21.1399942
Mean1947.327265
Median Absolute Deviation (MAD)1023.34
Skewness3.065454916
Sum73634285.89
Variance3021011.163
MonotocityNot monotonic
2020-12-07T09:39:54.065844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25001470.4%
 
3000950.3%
 
2000660.2%
 
1666.67570.2%
 
1500550.1%
 
3500530.1%
 
2333.33480.1%
 
1000430.1%
 
5000420.1%
 
500380.1%
 
Other values (22868)3716998.3%
 
ValueCountFrequency (%) 
0.751< 0.1%
 
1.041< 0.1%
 
1.091< 0.1%
 
1.411< 0.1%
 
2.431< 0.1%
 
ValueCountFrequency (%) 
330001< 0.1%
 
31976.741< 0.1%
 
270001< 0.1%
 
23629.231< 0.1%
 
21739.111< 0.1%
 

city
Categorical

HIGH CARDINALITY

Distinct902
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
KNOKKE-HEIST
 
1210
ANTWERPEN
 
1066
UCCLE
 
728
BRUXELLES
 
643
GENT
 
613
Other values (897)
33553 
ValueCountFrequency (%) 
KNOKKE-HEIST12103.2%
 
ANTWERPEN10662.8%
 
UCCLE7281.9%
 
BRUXELLES6431.7%
 
GENT6131.6%
 
OOSTENDE5811.5%
 
MIDDELKERKE5041.3%
 
BRUGGE4891.3%
 
IXELLES4431.2%
 
BLANKENBERGE4151.1%
 
Other values (892)3112182.3%
 
2020-12-07T09:39:54.346851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique80 ?
Unique (%)0.2%
2020-12-07T09:39:54.587624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length8
Mean length8.714542618
Min length2

type_of_property_num
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
1
20229 
2
17584 
ValueCountFrequency (%) 
12022953.5%
 
21758446.5%
 
2020-12-07T09:39:54.786254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:54.908633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:55.082875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

subtype_of_property_num
Real number (ℝ≥0)

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.882976754
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:55.272997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile15
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.285081877
Coefficient of variation (CV)1.103555892
Kurtosis3.705786263
Mean3.882976754
Median Absolute Deviation (MAD)2
Skewness2.07697252
Sum146827
Variance18.36192669
MonotocityNot monotonic
2020-12-07T09:39:55.571749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
11539940.7%
 
31290634.1%
 
525946.9%
 
1715554.1%
 
913063.5%
 
139702.6%
 
67201.9%
 
74281.1%
 
103200.8%
 
82580.7%
 
Other values (11)13573.6%
 
ValueCountFrequency (%) 
11539940.7%
 
22520.7%
 
31290634.1%
 
4890.2%
 
525946.9%
 
ValueCountFrequency (%) 
23230.1%
 
22570.2%
 
21730.2%
 
20790.2%
 
19620.2%
 

equiped_kitchen_num
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.273160024
Minimum-1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:55.919125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q33
95-th percentile5
Maximum8
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.972769843
Coefficient of variation (CV)1.549506586
Kurtosis-0.3579932249
Mean1.273160024
Median Absolute Deviation (MAD)2
Skewness0.6007856698
Sum48142
Variance3.891820853
MonotocityNot monotonic
2020-12-07T09:39:56.200680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
11383236.6%
 
-11132429.9%
 
3577815.3%
 
526176.9%
 
219215.1%
 
414363.8%
 
67261.9%
 
71590.4%
 
8200.1%
 
ValueCountFrequency (%) 
-11132429.9%
 
11383236.6%
 
219215.1%
 
3577815.3%
 
414363.8%
 
ValueCountFrequency (%) 
8200.1%
 
71590.4%
 
67261.9%
 
526176.9%
 
414363.8%
 

building_condition_num
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.441435485
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:56.398303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q33
95-th percentile5
Maximum6
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.852571319
Coefficient of variation (CV)1.285226664
Kurtosis-1.001975372
Mean1.441435485
Median Absolute Deviation (MAD)2
Skewness0.1214234423
Sum54505
Variance3.432020493
MonotocityNot monotonic
2020-12-07T09:39:56.554944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
11159830.7%
 
-11012026.8%
 
3980625.9%
 
422385.9%
 
520295.4%
 
219055.0%
 
61170.3%
 
ValueCountFrequency (%) 
-11012026.8%
 
11159830.7%
 
219055.0%
 
3980625.9%
 
422385.9%
 
ValueCountFrequency (%) 
61170.3%
 
520295.4%
 
422385.9%
 
3980625.9%
 
219055.0%
 

region_num
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.4 KiB
1
23667 
2
9538 
0
4608 
ValueCountFrequency (%) 
12366762.6%
 
2953825.2%
 
0460812.2%
 
2020-12-07T09:39:56.780241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-07T09:39:56.924574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:57.068767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

province_num
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.907835929
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Memory size295.4 KiB
2020-12-07T09:39:57.242399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.791049227
Coefficient of variation (CV)0.5686924475
Kurtosis-0.7744606279
Mean4.907835929
Median Absolute Deviation (MAD)2
Skewness0.3277874247
Sum185580
Variance7.789955787
MonotocityNot monotonic
2020-12-07T09:39:57.410053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
6702418.6%
 
2573715.2%
 
5504313.3%
 
1460812.2%
 
8407010.8%
 
3397410.5%
 
418895.0%
 
716004.2%
 
1114933.9%
 
912723.4%
 
ValueCountFrequency (%) 
1460812.2%
 
2573715.2%
 
3397410.5%
 
418895.0%
 
5504313.3%
 
ValueCountFrequency (%) 
1114933.9%
 
1011032.9%
 
912723.4%
 
8407010.8%
 
716004.2%
 

Interactions

2020-12-07T09:38:32.931783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:33.228311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:33.445417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:33.667090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:33.905583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:34.125752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:34.353255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:34.571826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:34.802627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:35.074754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:35.309425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:35.523869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:35.745161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:35.953358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:36.189369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:36.402808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:36.611655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:36.808304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:36.998152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:37.207967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:37.644386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:37.863591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:38.089203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:38.285093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:38.501151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:38.715492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:38.913437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:39.117422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:39.510737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:39.764022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:39.965549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:40.200939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:40.396406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:40.599309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:40.809514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:41.013163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:41.225944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:41.430328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:41.655411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:41.868072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:42.077094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:42.299967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:42.497388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:42.697426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:42.921186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:43.205340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:43.501600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:43.735094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:43.961089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:44.188381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:44.404681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:44.633347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:44.869890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:45.127085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:45.499431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:45.734668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:45.951327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:46.164687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:46.390329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:46.613141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:46.839415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:47.054425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:47.259725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:47.461265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:47.690727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:47.889927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:48.137446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:48.345454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:48.564343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:48.775189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:48.985109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:49.203222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:49.397021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:49.595054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:49.804487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:50.033819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:50.277443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:50.497890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:50.719134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:50.948476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:51.172128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:51.412799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:51.694234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:51.977155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:52.215250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:52.440193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:52.699623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:52.938720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:53.199392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:53.434463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:53.660980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:53.877101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:54.083036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:54.279835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:54.688478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:54.899195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:55.159230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:55.369178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:55.586313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:55.794672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:56.006306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:56.218472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:56.420133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:56.617057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:56.841394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:57.043543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:57.255814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:57.464331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:57.681452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:57.906638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:58.159984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:58.382174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:58.597668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:58.815038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:59.025391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:59.246052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:59.490580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:38:59.715119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:00.304140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:00.541428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:00.909245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:01.709389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:02.395345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:02.659533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:02.918814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:03.221909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:03.466337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:03.670379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:03.982640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:04.257609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:04.527599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:04.805151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:05.080432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:05.315281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:05.566661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:05.782801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:06.007318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:06.262591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:06.477855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:06.711094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:06.942075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:07.176896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:07.409440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:07.635653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:08.618249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:08.834472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:09.071147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:09.281401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:09.519701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:09.773666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:09.987169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:10.236181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:10.445662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:10.655624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:10.884313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:11.099655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:11.331056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:11.542727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:11.777669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:12.001769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:12.235569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:12.462197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:12.670474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:12.876741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:13.143244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:13.353714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:13.569741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:13.780907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:13.981291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:14.207971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:14.411252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:14.624451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:14.826257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:15.058996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:15.329648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:15.573868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:15.796436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:16.052767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:16.271698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:16.510855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:16.742734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:16.964111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:17.174114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:17.370831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:17.590494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:17.789339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:18.048338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:18.296943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:18.536546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:18.792878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:19.040861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:19.263299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:19.796367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:20.765445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:21.425546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:21.727194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:21.971147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:22.190232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:22.460000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:22.751478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:23.504730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:23.882025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:24.133200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:24.418706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:24.681682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:25.893289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:27.896799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:28.275320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:28.548110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:28.790419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:29.025961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:29.403747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:29.872904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:31.197603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:32.733419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:33.573803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:34.208677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:34.936150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:35.535371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:35.838715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:36.080616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:36.391879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:36.656491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:36.974195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:37.412619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-07T09:39:57.637467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-07T09:39:58.168295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-07T09:39:58.815045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-07T09:40:00.283098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-07T09:40:01.262561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-07T09:39:38.345228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T09:39:40.820536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

idlocalitytype_of_propertysubtype_of_propertypricenr_of_roomsareaequiped_kitchenopen_fireterraceterrace_areagardengarden_areatotal_land_areanr_of_facadesswimming_poolbuilding_conditionkitchenregionprovincesq_m_pricesq_m_land_pricecitytype_of_property_numsubtype_of_property_numequiped_kitchen_numbuilding_condition_numregion_numprovince_num
090440811083APARTMENTAPARTMENT265000490INSTALLED01130010340AS_NEW0BXLBXL2944.442572.82GANSHOREN111101
190439781000APARTMENTAPARTMENT17950004650USA_HYPER_EQUIPPED1140000105030AS_NEW0BXLBXL2761.541709.52BRUXELLES112101
290410954860HOUSEHOUSE3200005231NOT_INSTALLED013011200142130AS_NEW1WALWLG1385.28225.19PEPINSTER234129
390430369600APARTMENTAPARTMENT195000275INSTALLED00-1007520GOOD0VLAVOV2600.002600.00RENAIX111315
490429506010APARTMENTTRIPLEX2350003149HYPER_EQUIPPED01150016420AS_NEW0WALWHT1577.181432.93COUILLET143128
590420731070APARTMENTAPARTMENT3200003130USA_HYPER_EQUIPPED01140014420AS_NEW0BXLBXL2461.542222.22ANDERLECHT112101
690422677181HOUSEVILLA3250002130INSTALLED01301600104340TO_BE_DONE_UP0WALWHT2500.00311.60SENEFFE251428
790425115340HOUSEVILLA5690006324INSTALLED015711821220240JUST_RENOVATED0WALWNA1756.17258.40GESVES2512211
890344941950APARTMENTAPARTMENT7150002126USA_HYPER_EQUIPPED0160013220AS_NEW0VLAVBR5674.605416.67KRAAINEM112113
990390191050APARTMENTAPARTMENT15500003213USA_HYPER_EQUIPPED013900252-10AS_NEW0BXLBXL7277.006150.79IXELLES112101

Last rows

idlocalitytype_of_propertysubtype_of_propertypricenr_of_roomsareaequiped_kitchenopen_fireterraceterrace_areagardengarden_areatotal_land_areanr_of_facadesswimming_poolbuilding_conditionkitchenregionprovincesq_m_pricesq_m_land_pricecitytype_of_property_numsubtype_of_property_numequiped_kitchen_numbuilding_condition_numregion_numprovince_num
3780390386953000HOUSEHOUSE9400006150UNK00-100150-10UKN1VLAVBR6266.676266.67LEUVEN23-1-113
3780487049371840APARTMENTAPARTMENT3100002102UNK011200114-10UKN1VLAVBR3039.222719.30LONDERZEEL11-1-113
3780587214131800HOUSEHOUSE4549933169UNK00-1122038930UKN1VLAVBR2692.271169.65VILVOORDE23-1-113
3780690629503000APARTMENTFLAT_STUDIO199900026UNK00-1002620UKN1VLAVBR7688.467688.46LEUVEN16-1-113
3780790231871570APARTMENTAPARTMENT215708176UNK0170083-10UKN1VLAVBR2838.262598.89GALMAARDEN11-1-113
3780890231901570APARTMENTAPARTMENT228208283UNK0170090-10UKN1VLAVBR2749.492535.64GALMAARDEN11-1-113
3780980660061800APARTMENTAPARTMENT228000186UNK0114134134-10UKN1VLAVBR2651.161701.49VILVOORDE11-1-113
3781089665251500APARTMENTAPARTMENT3060003110UNK01500115-10UKN1VLAVBR2781.822660.87HALLE11-1-113
3781189665231500APARTMENTAPARTMENT219000165UNK01220087-10UKN1VLAVBR3369.232517.24HALLE11-1-113
3781288585901910APARTMENTDUPLEX2750002100UNK011916318220UKN1VLAVBR2750.001510.99KAMPENHOUT19-1-113